np.array() from Python lists in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.array() do when given a Python list?

np.array() converts a Python list into a NumPy array, which is a grid of values all of the same type. This allows for fast math and data operations.

beginner

How do you create a 2D NumPy array from a list of lists?

Pass a list of lists to np.array(). Each inner list becomes a row in the 2D array.

arr = np.array([[1, 2], [3, 4]])

beginner

What is the difference between a Python list and a NumPy array created from it?

A Python list can hold different data types and is slower for math. A NumPy array has one data type and is optimized for fast math and memory use.

intermediate

Can np.array() handle lists with mixed data types? What happens?

Yes, but NumPy will convert all elements to a common data type, usually upcasting to a type that can hold all values (like strings if mixed types). This can affect performance.

beginner

How can you check the shape of a NumPy array created from a list?

Use the .shape attribute to see the dimensions (rows, columns) of the array.

arr = np.array([[1, 2], [3, 4]])
print(arr.shape)  # Output: (2, 2)

What does np.array([1, 2, 3]) return?

A A Python tuple with elements 1, 2, 3

B A Python list with elements 1, 2, 3

C An error because input is a list

D A NumPy array with elements 1, 2, 3

np.array() converts the list into a NumPy array, not a list or tuple.

How do you create a 2D NumPy array from lists?

A np.array([[1, 2], [3, 4]])

B np.array([1, 2, 3])

C np.array(1, 2, 3, 4)

D np.array({1:2, 3:4})

Passing a list of lists creates a 2D array.

What attribute shows the shape of a NumPy array?

A .size

B .shape

C .length

D .type

.shape gives the dimensions of the array.

If a list has mixed types, what does np.array() do?

A Converts all to a common type

B Creates a list instead

C Throws an error

D Keeps original types in array

NumPy converts all elements to a single common type.

Why use NumPy arrays instead of Python lists?

A NumPy arrays are slower

B NumPy arrays use more memory

C NumPy arrays allow fast math operations

D Python lists support only numbers

NumPy arrays are optimized for fast math and use less memory.

Explain how to create a NumPy array from a Python list and why it is useful.

Think about what changes when you convert a list to an array.

Describe what happens if you create a NumPy array from a list with mixed data types.

Consider how NumPy handles different types in one array.